Visitors to Australia's federal parliament are often surprised by the robust verbal confrontation between the government and the opposition – technically known as questions without notice, more commonly as question time. A theatrical highpoint of every sitting day, question time is part intellectual cage fight, part kindergarten spat – and all psychological warfare. Political journalists watch the hour-long question time as drought-stricken farmers view the clouds. They look for signs, they read the climate. But what if you were interested in facts?

Gradescope, which has graded millions of exam questions, has made the grade itself. The company has raised a 2.6 million round of funding from Freestyle Capital, Bloomberg Beta, Reach Capital and the House Fund. Existing investor K9 Ventures also participated. Dave Samuel from Freestyle will be joining Manu Kumar from K9 Ventures on Gradescope's board. The company, started as a side project at the University of California Berkeley in 2012, makes a software that helps science and engineering professors and teaching assistants grade exam questions on handwritten tests.

IT and analytics managers struggling with all the data flooding into their organizations may find it hard to ignore the increased marketing push machine learning tools are getting from technology vendors. And for good reason: Running automated algorithms designed to learn on their own as they churn through large data sets can accelerate data mining and predictive analytics applications -- and give users information they might not get otherwise. But companies looking to take advantage of machine learning often face a substantial learning curve. For starters, a lot of big data infrastructure technologies -- Hadoop, the Spark processing engine and related open source software in particular -- typically underlie machine learning efforts. In many cases, that means building a suitable data processing and management architecture from scratch.

Niall Dunne is the Chief Sustainability Officer for BT, working with BT's Chief Executive, Chairman and executive management team to bring the company's purpose, to use the power of communications to make a better world, to life. Before joining BT in 2011, Niall was Managing Director in Europe, the Middle East and Africa (EMEA) at Saatchi & Saatchi. Prior to that, Dunne was an executive at Accenture, where he helped establish the company's climate change and sustainability practice. Dunne has written and spoken about the power of communications to tackle major social, environmental and economic problems. Niall was vice chair of the WEF's Global Agenda Council on Sustainable Consumption 2012-14 and joined the WEF Global Agenda Council on Climate Change in 2014.

Chat applications are becoming a mainstream trend and our preferred way of interacting with colleagues, friends and family. From the early days of SMS to the favorite snaps of our children, real-time online conversations are everywhere and here to stay. The acquisition of WhatAapp by Facebook in 2014 for a hefty 22 Billion price tag made it clear and promising as TechCrunch noticed it one year later. But although TechCrunch saw messaging apps as the future of mobile portal, they remained more or less next to the Internet, without a direct impact, except their increasing audience. The recent surge of interest in Bots and AI is changing the game and we'll be witnessing the second major fragmentation of the Internet.

First there was Artificial Intelligence (AI), then came machine learning... neural networks and finally cognitive computing technology. But then came multilingual cognitive computing technology. Cogito Studio is a product for developing customised semantic applications for text analytics, including information analysis, categorisation and extraction. Developed by Expert System in the US state of Maryland, Cogito Studio combines a cocktail of AI algorithms for simulating the human ability to read and understand language (semantics) and deep learning techniques (machine learning) to help optimise the creation of applications that are advanced, intelligent and intuitive.

Of the all the burning issues discussed at the World Economic Forum in Davos, Switzerland, earlier this year, one that got top billing was the promise and peril of the "Fourth Industrial Revolution." What does this Fourth Industrial Revolution mean for enterprises and their various stakeholders? What should they do to keep pace with it and create competitive advantage to come out ahead? The Third Industrial Revolution--the digital age that began in the mid-20th century--was about computerization. The Fourth Industrial Revolution we are experiencing today builds on this first wave of computerization with the latest, rapidly evolving and disruptive advances in technology: the Internet of Things, the Industrial Internet, robotic process automation, autonomous vehicles, artificial intelligence, 3D printing, cyber-physical systems and connected wearable devices.

Imagine this: your hospital administrator asks you to help reduce the length of inpatient stays and they need a plan within a week. Chances are, most of you couldn't. But, the technology to mine and analyze your data does exist. Much like your daily Google searches, it's possible to input your search criteria, click Enter, and have answers at your fingertips in seconds. Doing so is part of radiology's push toward using big data, said Woojin Kim, MD, director of innovation at Montage Healthcare Solutions, Inc. "Radiology doesn't yet have big data like other industries, but that's changing rapidly. People want access to data to be able to turn insight into action," he said.

Chances are, you're already hip-deep in machine-learning applications. It's how Google Photo organizes those pictures from your vacation in Spain. It's how Facebook suggests tags for the pictures you took at last week's soccer match. It's how the cars of nearly every major automaker can help you avoid unsafe lane changes. Machine learning – which enables a computer to learn without new programming – is exploding in its ability to handle highly complex tasks.

In my last article, I discussed the fundamentals of deep learning, where I explained the basic working of a artificial neural network. If you've been following this series, today we'll become familiar with practical process of implementing neural network in Python (using Theano package). I found various other packages also such as Caffe, Torch, TensorFlow etc to do this job. But, Theano is no less than and satisfactorily execute all the tasks. Also, it has multiple benefits which further enhances the coding experience in Python. In this article, I'll provide a comprehensive practical guide to implement Neural Networks using Theano.